library(shiny)
library(shinydashboard)
library(ggmap)
library(ggplot2)
library(data.table)
library(plotly)
library(waffle)
library(ggthemes)
library(extrafont)
library(rsconnect)

Periodo analizado

Se analiza el periodo Novimebre 2017 - Junio 2017 por mantenerse constante el precio del boleto en $7,50

Mapa Estaciones de subte

#setwd("~/Documents/Visualizacion")
estaciones <- read.csv('estaciones-de-subte.csv', header = TRUE)
map.bs.as <- get_map( location = "Buenos Aires"
                , zoom = 12
                , maptype = "terrain"
                , color = "bw" 
)
Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Buenos+Aires&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Buenos%20Aires&sensor=false
cols <- c("D" = "chartreuse4", "B" = "red", "C" = "blue", "H" = "darkgoldenrod1" , 'A' = "cornflowerblue", 'E' = 'blueviolet')
colors <- c("LINEA_D" = "chartreuse4", "LINEA_B" = "red", "LINEA_C" = "blue", "LINEA_H" = "darkgoldenrod1" , 'LINEA_A' = "cornflowerblue", 'LINEA_E' = 'blueviolet')

Plot 2: Ubicacion geografica de las estaciones

p <- ggmap(map.bs.as) +
      geom_point(data = estaciones ,
                    aes(x = X, y = Y,
                    colour = factor (LINEA))) +
      scale_colour_manual(values = cols) +
      theme(
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks.x = element_blank(),
      axis.ticks.y = element_blank(),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      legend.title = element_text("LINEAS"))
p

#molinetes.2017 <- fread("molinetes_historico.csv")
#head(molinetes.2017)
#molinetes.2017.estacion.hs <- molinetes.2017[, .(prom = round(mean(TOTAL))), by = list(LINEA,ESTACION, DESDE,ID)]
#unique(filter(molinetes.2017.estacion.hs, LINEA == 'LINEA_D')$ESTACION)

Plot 3: Latido de estaciones

#molinetes.2017.estacion.hs <- molinetes.2017.estacion.hs[estaciones, on = 'ID']
#write.table(molinetes.2017.estacion.hs, "molinetes_2017_estacion_hs.csv", sep =",", col.names = TRUE, row.names = #FALSE)
molinetes.2017.estacion.hs <- read.table(file = "molinetes_2017_estacion_hs.csv", sep = ",", header = TRUE)
ax <- list(
  title = "",
  zeroline = FALSE,
  showline = FALSE,
  showticklabels = FALSE,
  showgrid = FALSE
)
p <- plot_ly(
    data = molinetes.2017.estacion.hs,
    x = ~ X, 
    y =  ~ Y, 
    size = ~ prom,
    color = ~ LINEA, 
    colors = ~ colors,
    frame = ~ DESDE, 
    text = ~ESTACION, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) 
p <- layout(p,
            xaxis=ax,
            yaxis=ax)
p <- animation_opts(p, frame = 100)
p

Plot 4: Densidad pasajeros por estacion

q <- plot_ly(
    data = molinetes.2017.estacion.hs,
    x = ~ prom, 
    y =  ~ ESTACION, 
    size = ~ prom, 
    color = ~ LINEA, 
    colors = ~ colors,
    frame = ~ DESDE, 
    text = ~ESTACION, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) 
q <- layout(q,
            xaxis=list(
            showline = FALSE,
            showgrid = FALSE,
            title = 'Cantidad de personas promedio'
            ),
            yaxis = list(showgrid = FALSE, title = F),
            font = list(family = "sans serif", size = 8, color = "grey")
            )
q <- q %>%
  animation_slider(currentvalue = list( font = list(color="red")))
q

Plot 4: Waffle Chart

#molinetes.2017.cant.pasajeros <- molinetes.2017[, .(sum(TOTAL)), by = PERIODO]
#molinetes.2017.cant.pasajeros.linea <- molinetes.2017[, .(cant = sum(TOTAL)), by = LINEA]
#molinetes.2017.avg.linea <- molinetes.2017[, .(avg = round(mean(TOTAL))), by = LINEA]
#write.table(molinetes.2017.avg.linea, "molinetes_2017_avg_linea.csv", sep =",", col.names = TRUE, row.names = #FALSE)
molinetes.2017.avg.linea <- read.table(file = "molinetes_2017_avg_linea.csv", sep = ",", header = TRUE)
vals = as.vector(unlist (round(molinetes.2017.avg.linea[,"avg"]), use.names = TRUE))
names (vals) = unlist (molinetes.2017.avg.linea[,"LINEA"], use.names = FALSE)

use_glyph = ‘user’, glyph_size = 5,

waffle(vals , rows = 5, colors = colors , size = 0.5, xlab = "1 icono == 1 persona", legend_pos = "down")

Shiny Dashboard

skin <- "yellow"
header <-  dashboardHeader(title = span(tagList(icon("subway", lib ="font-awesome"), 
                          "SUBTES Ciudad de Buenos Aires - Maria Ines Aran")),
                          titleWidth = 550)
sidebar <- dashboardSidebar(disable = TRUE)
                            
body <- dashboardBody(
                      
                      fluidRow(box(h4("Analisis de cantidad de pasajeros que ingresan a las estaciones de subte de la Ciudad de Buenos Aires en el periodo Enero a Septiembre 2017. Basado en datos publicados por https://data.buenosaires.gob.ar/
"),
                                   width = 12)),
                      fluidRow(box(h6("Trabajo final de la materia Visualizacion - Esp. Cs. de datos del ITBA - Maria Ines Aran"),
                                   width = 12)),
                      fluidRow(
                               box(title = "Ubicacion geografica de lineas de subte de Ciudad de Buenos Aires",
                                  background = "yellow",
                                  plotOutput("plot2")),
                               box(title = "Pasajeros por linea",
                                   "Cantidad de pasajeros promedio por linea de subtes de Ciudad de Buenos Aires   durante 2017",
                                   background = "yellow",
                                   width = 6,
                                   solidHeader = TRUE,
                                   plotOutput("plot1", height = 250))),
                      fluidRow(
                              box( title = "Densidad de pasajeros por horario (Play!)",
                                   background = "yellow",
                                   status = "warning",
                                   width = 12,
                                   plotlyOutput("plot4", height = 1000),verbatimTextOutput("event")))
                      
                      )
                

Shiny App

ui <- dashboardPage(
      header,
      sidebar,
      body,
      skin = skin)
server <- function(input, output) {
  #Plot 1
  output$plot1 <- renderPlot({
    waffle(vals , rows = 5, colors = colors , size = 0.5, xlab = "1 icono == 1 persona", legend_pos = "down")
  })
  #Plot 2
  output$plot2 <- renderPlot({
    ggmap(map.bs.as) +
      geom_point(data = estaciones ,
                    aes(x = X, y = Y,
                    colour = factor (LINEA))) +
      scale_colour_manual(values = cols) +
      theme(
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks.x = element_blank(),
      axis.ticks.y = element_blank(),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      legend.title = element_text("LINEAS"))
  })
  #Plot 4
  output$plot4 <- renderPlotly({
    q <- plot_ly(
    data = molinetes.2017.estacion.hs,
    x = ~ prom, 
    y =  ~ ESTACION, 
    size = ~ prom, 
    color = ~ LINEA, 
    colors = ~ colors,
    frame = ~ DESDE, 
    text = ~ESTACION, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) 
q <- layout(q,
            xaxis=list(
            showline = FALSE,
            showgrid = FALSE,
            title = 'Cantidad de personas promedio'
            ),
            yaxis = list(showgrid = FALSE, title = F),
            font = list(family = "sans serif", size = 8, color = "grey")
            )
q <- q %>%
  animation_slider(currentvalue = list( font = list(color="red")))    
  })
  
  
}
options(shiny.sanitize.errors = TRUE)
shinyApp(ui, server)

Listening on http://127.0.0.1:5917
---
title: "R Notebook"
output:
  html_document: default
  html_notebook: default
  pdf_document: default
---
```{r}
library(shiny)
library(shinydashboard)
library(ggmap)
library(ggplot2)
library(data.table)
library(plotly)
library(waffle)
library(ggthemes)
library(extrafont)
library(rsconnect)
```


##Periodo analizado
Se analiza el periodo  Novimebre 2017 - Junio 2017 por mantenerse constante el precio del boleto en $7,50

##Mapa Estaciones de subte

```{r}
#setwd("~/Documents/Visualizacion")
estaciones <- read.csv('estaciones-de-subte.csv', header = TRUE)
```

```{r}
map.bs.as <- get_map( location = "Buenos Aires"
                , zoom = 12
                , maptype = "terrain"
                , color = "bw" 
)
```

```{r}
cols <- c("D" = "chartreuse4", "B" = "red", "C" = "blue", "H" = "darkgoldenrod1" , 'A' = "cornflowerblue", 'E' = 'blueviolet')
```

```{r}
colors <- c("LINEA_D" = "chartreuse4", "LINEA_B" = "red", "LINEA_C" = "blue", "LINEA_H" = "darkgoldenrod1" , 'LINEA_A' = "cornflowerblue", 'LINEA_E' = 'blueviolet')
```

#Plot 2: Ubicacion geografica de las estaciones
```{r}
p <- ggmap(map.bs.as) +
      geom_point(data = estaciones ,
                    aes(x = X, y = Y,
                    colour = factor (LINEA))) +
      scale_colour_manual(values = cols) +
      theme(
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks.x = element_blank(),
      axis.ticks.y = element_blank(),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      legend.title = element_text("LINEAS"))
p
```

```{r}
#molinetes.2017 <- fread("molinetes_historico.csv")
```

```{r}
#head(molinetes.2017)
```

```{r}
#molinetes.2017.estacion.hs <- molinetes.2017[, .(prom = round(mean(TOTAL))), by = list(LINEA,ESTACION, DESDE,ID)]
```


```{r}
#unique(filter(molinetes.2017.estacion.hs, LINEA == 'LINEA_D')$ESTACION)
```

#Plot 3: Latido de estaciones
```{r}
#molinetes.2017.estacion.hs <- molinetes.2017.estacion.hs[estaciones, on = 'ID']
```

```{r}
#write.table(molinetes.2017.estacion.hs, "molinetes_2017_estacion_hs.csv", sep =",", col.names = TRUE, row.names = #FALSE)
```

```{r}
molinetes.2017.estacion.hs <- read.table(file = "molinetes_2017_estacion_hs.csv", sep = ",", header = TRUE)
```


```{r}
ax <- list(
  title = "",
  zeroline = FALSE,
  showline = FALSE,
  showticklabels = FALSE,
  showgrid = FALSE
)
```


```{r}
p <- plot_ly(
    data = molinetes.2017.estacion.hs,
    x = ~ X, 
    y =  ~ Y, 
    size = ~ prom,
    color = ~ LINEA, 
    colors = ~ colors,
    frame = ~ DESDE, 
    text = ~ESTACION, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) 

p <- layout(p,
            xaxis=ax,
            yaxis=ax)
p <- animation_opts(p, frame = 100)
p
```


# Plot 4: Densidad pasajeros por estacion
```{r}
q <- plot_ly(
    data = molinetes.2017.estacion.hs,
    x = ~ prom, 
    y =  ~ ESTACION, 
    size = ~ prom, 
    color = ~ LINEA, 
    colors = ~ colors,
    frame = ~ DESDE, 
    text = ~ESTACION, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) 

q <- layout(q,
            xaxis=list(
            showline = FALSE,
            showgrid = FALSE,
            title = 'Cantidad de personas promedio'
            ),
            yaxis = list(showgrid = FALSE, title = F),
            font = list(family = "sans serif", size = 8, color = "grey")
            )
q <- q %>%
  animation_slider(currentvalue = list( font = list(color="red")))
q
```


#Plot 4: Waffle Chart
```{r}
#molinetes.2017.cant.pasajeros <- molinetes.2017[, .(sum(TOTAL)), by = PERIODO]
```

```{r}
#molinetes.2017.cant.pasajeros.linea <- molinetes.2017[, .(cant = sum(TOTAL)), by = LINEA]
```

```{r}
#molinetes.2017.avg.linea <- molinetes.2017[, .(avg = round(mean(TOTAL))), by = LINEA]
```

```{r}
#write.table(molinetes.2017.avg.linea, "molinetes_2017_avg_linea.csv", sep =",", col.names = TRUE, row.names = #FALSE)
```

```{r}
molinetes.2017.avg.linea <- read.table(file = "molinetes_2017_avg_linea.csv", sep = ",", header = TRUE)
```

```{r}
vals = as.vector(unlist (round(molinetes.2017.avg.linea[,"avg"]), use.names = TRUE))
names (vals) = unlist (molinetes.2017.avg.linea[,"LINEA"], use.names = FALSE)
```


use_glyph = 'user', glyph_size = 5,

```{r}
waffle(vals , rows = 5, colors = colors , size = 0.5, xlab = "1 icono == 1 persona", legend_pos = "down")
```

#Shiny Dashboard

```{r}
skin <- "yellow"
header <-  dashboardHeader(title = span(tagList(icon("subway", lib ="font-awesome"), 
                          "SUBTES Ciudad de Buenos Aires - Maria Ines Aran")),
                          titleWidth = 550)
sidebar <- dashboardSidebar(disable = TRUE)
                            
body <- dashboardBody(
                      
                      fluidRow(box(h4("Analisis de cantidad de pasajeros que ingresan a las estaciones de subte de la Ciudad de Buenos Aires en el periodo Enero a Septiembre 2017. Basado en datos publicados por https://data.buenosaires.gob.ar/
"),
                                   width = 12)),
                      fluidRow(box(h6("Trabajo final de la materia Visualizacion - Esp. Cs. de datos del ITBA - Maria Ines Aran"),
                                   width = 12)),
                      fluidRow(
                               box(title = "Ubicacion geografica de lineas de subte de Ciudad de Buenos Aires",
                                  background = "yellow",
                                  plotOutput("plot2")),
                               box(title = "Pasajeros por linea",
                                   "Cantidad de pasajeros promedio por linea de subtes de Ciudad de Buenos Aires   durante 2017",
                                   background = "yellow",
                                   width = 6,
                                   solidHeader = TRUE,
                                   plotOutput("plot1", height = 250))),
                      fluidRow(
                              box( title = "Densidad de pasajeros por horario (Play!)",
                                   background = "yellow",
                                   status = "warning",
                                   width = 12,
                                   plotlyOutput("plot4", height = 1000),verbatimTextOutput("event")))
                      
                      )
                
```

#Shiny App
```{r}
ui <- dashboardPage(
      header,
      sidebar,
      body,
      skin = skin)

server <- function(input, output) {
  #Plot 1
  output$plot1 <- renderPlot({
    waffle(vals , rows = 5, colors = colors , size = 0.5, xlab = "1 icono == 1 persona", legend_pos = "down")
  })
  #Plot 2
  output$plot2 <- renderPlot({
    ggmap(map.bs.as) +
      geom_point(data = estaciones ,
                    aes(x = X, y = Y,
                    colour = factor (LINEA))) +
      scale_colour_manual(values = cols) +
      theme(
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks.x = element_blank(),
      axis.ticks.y = element_blank(),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      legend.title = element_text("LINEAS"))
  })
  #Plot 4
  output$plot4 <- renderPlotly({
    q <- plot_ly(
    data = molinetes.2017.estacion.hs,
    x = ~ prom, 
    y =  ~ ESTACION, 
    size = ~ prom, 
    color = ~ LINEA, 
    colors = ~ colors,
    frame = ~ DESDE, 
    text = ~ESTACION, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) 

q <- layout(q,
            xaxis=list(
            showline = FALSE,
            showgrid = FALSE,
            title = 'Cantidad de personas promedio'
            ),
            yaxis = list(showgrid = FALSE, title = F),
            font = list(family = "sans serif", size = 8, color = "grey")
            )
q <- q %>%
  animation_slider(currentvalue = list( font = list(color="red")))    
  })
  
  
}

options(shiny.sanitize.errors = TRUE)

shinyApp(ui, server)
```


